Quantifying nonrandomness in evolving networks

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dc.contributor.author Pandey, Pradumn Kumar
dc.contributor.author Singh, Mayank
dc.date.accessioned 2020-10-21T05:35:28Z
dc.date.available 2020-10-21T05:35:28Z
dc.date.issued 2020-10
dc.identifier.citation Pandey, Pradumn Kumar and Singh, Mayank, "Quantifying nonrandomness in evolving networks", IEEE Transactions on Computational Social Systems, DOI: 10.1109/TCSS.2020.3025296, Oct. 2020. en_US
dc.identifier.issn 2329-924X
dc.identifier.uri https://ieeexplore.ieee.org/document/9210878
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/5776
dc.description.abstract Complex systems have been successfully modeled as networks exhibiting the varying extent of randomness and nonrandomness. Network scientists contemplate randomness as one of the most desirable characteristics for real complex systems efficient performance. However, the current methodologies for randomness (or nonrandomness) quantification are nontrivial. In this article, we empirically showcase severe limitations associated with the state-of-the-art graph spectral-based quantification approaches. Addressing these limitations led to the proposal of a novel spectrum-based methodology that leverages configuration models as a reference network to quantify the nonrandomness in a given candidate network. Besides, we derive mathematical formulations for demonstrating the dependence of nonrandomness on three structural properties: modularity, clustering, and the highest degree nodes growth rate. We also introduce a novel graph signature (termed "cumulative spectral difference") to visualize the nonrandomness in the network. Later, this article also discusses the relationship between the proposed nonrandomness measure and the diffusion affinity of networks. Toward the end, this article extensively discusses observations emerging from these signatures for both real-world and simulated networks.
dc.description.statementofresponsibility by Pradumn Kumar Pandey and Mayank Singh
dc.language.iso en_US en_US
dc.publisher Institute of Electrical and Electronics Engineers en_US
dc.subject Eigenvalues en_US
dc.subject Eigenfunctions en_US
dc.subject Computational Modeling en_US
dc.subject Stochastic Processes en_US
dc.subject Complex Systems en_US
dc.subject Visualization en_US
dc.subject Twitter en_US
dc.subject Network Evolution en_US
dc.subject Network Spectra en_US
dc.subject Randomness en_US
dc.title Quantifying nonrandomness in evolving networks en_US
dc.type Article en_US
dc.relation.journal IEEE Transactions on Computational Social Systems

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